Zapier Integration

Zapier gets the data moving. Custom AI decides what to do with it.

Zapier is good at moving structured data between apps when the logic is simple. The gap is classification, extraction, and decisions that require understanding unstructured content. A custom AI step sits in your Zap and handles those decisions — the rest of your workflow continues as normal.

You keep your existing Zaps. We add an AI step that reads the data, makes a decision, and passes structured output to the next step. No rebuilding your automation stack.

How we work

Tell us which Zap needs intelligence.

What we build.

Six types of AI steps that plug into your existing Zapier workflows.

Custom classification node

A webhook step your Zap calls that classifies any input (an email body, a form submission, a support ticket) and returns a structured label your Zap branches on. Trained on your categories, not generic ones.

Extraction from unstructured text

Pass in raw text (a lead email, a meeting transcript, a document) and get back structured JSON with the fields you specify. The Zap continues with clean data instead of a blob of text no downstream app can use.

Summarisation steps

Condense long content (email threads, documents, transcripts) to a specified length and format. The summary goes into the next step of your Zap instead of the full text. Works with Slack notifications, CRM notes, or any text field.

Conditional branching based on AI output

Route your Zap down different paths based on what the AI decides: high-priority leads go one way, low-priority another; positive sentiment triggers one action, negative triggers a different one. AI makes the decision; Zapier executes the path.

Validation before push

Before your Zap writes to a CRM, database, or external system, an AI step validates the data: checks for required fields, normalises formats, flags anomalies, and can enrich missing values. Bad data stops before it enters your systems.

Domain-specific generation

Generate text that requires domain knowledge your Zap doesn’t have: personalised email copy from lead data, a response draft from a support ticket, a meeting agenda from a calendar invite. Generated with your rules, not a generic prompt.

Common use cases.

Specific Zap workflows that benefit from a custom AI step in the middle.

Lead routing by intent classification

A Zap fires when a new form submission arrives. Instead of routing by a single field, a custom AI step reads the full message, classifies the lead by intent and fit, and returns a routing label. High-fit demo requests go to a senior rep; pricing questions go to a self-serve sequence; competitor questions go to a competitive playbook.

Support ticket pre-processing

Support emails arrive in a Gmail trigger. An AI step extracts the issue type, urgency, and relevant product area, then creates a Zendesk ticket with the right fields already populated. The agent opens a structured ticket instead of a raw email.

Invoice data extraction to spreadsheet

PDF invoices arrive by email. An AI step extracts vendor, amount, line items, and due date. A downstream step creates a row in a Google Sheet or Airtable base. The finance team gets structured data without opening each PDF.

CRM note summarisation before Slack notify

When a deal changes stage in HubSpot or Salesforce, a Zap fires. An AI step reads the full deal notes and generates a one-paragraph summary. The Slack notification the sales team receives has context, not just “Deal X moved to Proposal.”

Why custom over Zapier’s built-in AI actions.

Zapier has native AI steps. Here is why teams with real data complexity build custom instead.

Zapier’s AI steps use generic prompts
Zapier has built-in AI actions, but they use generic prompts and general-purpose models. Custom AI steps use prompts calibrated to your data schema, your category definitions, and your domain vocabulary. The difference in accuracy on real-world data is significant.
Your schema needs to be defined
A generic extraction step doesn’t know which fields matter for your use case. A custom step is built against your specific output schema, the exact JSON structure your downstream app expects. No post-processing or field remapping needed.
You need reliability, not demos
Production Zaps run thousands of times. Custom AI steps are built with error handling, retry logic, fallback behaviour for edge cases, and logging you can inspect. Zapier’s native AI actions don’t give you that level of control.

Frequently asked questions.

Which Zapier plan is required for custom webhook steps?: The Webhooks by Zapier action is available on Starter plans and above. Professional and Team plans allow more tasks per month and multi-step Zaps, which is what you’ll need for complex workflows. We integrate via a webhook endpoint your Zap calls, no custom Zapier app required, so there are no app-approval delays.
Does a custom AI step add significant latency to my Zap?: Typically 1–3 seconds for classification and extraction tasks, 3–8 seconds for generation tasks, depending on input length and model used. For most Zap workflows, this is acceptable. If your use case requires sub-second response, we’ll discuss whether the workflow can be restructured or whether a different integration pattern is more appropriate.
When should I use Zapier for this versus replacing Zapier entirely?: Zapier is worth keeping when you need connections to dozens of apps and your logic is relatively simple. Replace it when your workflow requires stateful processing, complex branching that would take 20+ Zap steps, or data volumes that exhaust your task limits. Many clients keep Zapier for the app connections and use custom AI for the intelligence layer in the middle.
Can I use this as a Zapier Code step or does it require a webhook?: Both patterns work. The webhook approach (calling an external endpoint) is simpler to maintain and deploy. The Code step approach (JavaScript or Python running inside Zapier) works for lighter logic but has limitations: no external HTTP requests by default, 30-second timeout, and limited libraries. For production AI steps, the webhook pattern is more reliable.

Ready to add intelligence to your Zaps?

Tell us which workflow you want to improve and what decision or extraction you need in the middle of it. We’ll reply within one business day.

Zapier gets the data moving. Custom AI decides what to do with it.

You keep your existing Zaps. We add an AI step that reads the data, makes a decision, and passes structured output to the next step. No rebuilding your automation stack.

What we build.

Six types of AI steps that plug into your existing Zapier workflows.

Custom classification node

Extraction from unstructured text

Summarisation steps

Conditional branching based on AI output

Validation before push

Domain-specific generation

Common use cases.

Specific Zap workflows that benefit from a custom AI step in the middle.

Lead routing by intent classification

Support ticket pre-processing

Invoice data extraction to spreadsheet

CRM note summarisation before Slack notify

Why custom over Zapier’s built-in AI actions.

Zapier has native AI steps. Here is why teams with real data complexity build custom instead.

Zapier’s AI steps use generic prompts

Zapier has built-in AI actions, but they use generic prompts and general-purpose models. Custom AI steps use prompts calibrated to your data schema, your category definitions, and your domain vocabulary. The difference in accuracy on real-world data is significant.

Your schema needs to be defined

A generic extraction step doesn’t know which fields matter for your use case. A custom step is built against your specific output schema, the exact JSON structure your downstream app expects. No post-processing or field remapping needed.

You need reliability, not demos

Production Zaps run thousands of times. Custom AI steps are built with error handling, retry logic, fallback behaviour for edge cases, and logging you can inspect. Zapier’s native AI actions don’t give you that level of control.

Frequently asked questions.

Which Zapier plan is required for custom webhook steps?

The Webhooks by Zapier action is available on Starter plans and above. Professional and Team plans allow more tasks per month and multi-step Zaps, which is what you’ll need for complex workflows. We integrate via a webhook endpoint your Zap calls, no custom Zapier app required, so there are no app-approval delays.

Does a custom AI step add significant latency to my Zap?

Typically 1–3 seconds for classification and extraction tasks, 3–8 seconds for generation tasks, depending on input length and model used. For most Zap workflows, this is acceptable. If your use case requires sub-second response, we’ll discuss whether the workflow can be restructured or whether a different integration pattern is more appropriate.

When should I use Zapier for this versus replacing Zapier entirely?

Zapier is worth keeping when you need connections to dozens of apps and your logic is relatively simple. Replace it when your workflow requires stateful processing, complex branching that would take 20+ Zap steps, or data volumes that exhaust your task limits. Many clients keep Zapier for the app connections and use custom AI for the intelligence layer in the middle.

Can I use this as a Zapier Code step or does it require a webhook?

Both patterns work. The webhook approach (calling an external endpoint) is simpler to maintain and deploy. The Code step approach (JavaScript or Python running inside Zapier) works for lighter logic but has limitations: no external HTTP requests by default, 30-second timeout, and limited libraries. For production AI steps, the webhook pattern is more reliable.

Ready to add intelligence to your Zaps?

Tell us which workflow you want to improve and what decision or extraction you need in the middle of it. We’ll reply within one business day.